Planning semi-automated precast production using GA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it